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The Structure of Canonical Graphs for Second Order Spectral Null Constraints

The Structure of Canonical Graphs for Second Order Spectral Null Constraints
application/pdf Canonical graphs for spectral null constraints are important in designing spectral null codes for digital data storage devices. The structure of canonical graphs for second-order spectral null constraints is investigated. It is shown that for almost all blocks there is an irreducible component that generates the block.
openaire  

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

High fat diet-induced loss of pituitary plasticity in aging female mice with ablated leptin signaling in somatotropes. [PDF]

Front Endocrinol (Lausanne)
Miles TK, Odle AK, Byrum SD, Lagasse AN, Haney AC, Ortega VG, Herdman AK, MacNicol MC, MacNicol AM, Childs GV.   +9 more
europepmc   +1 more source

Multiparameter Symbolic Sensitivity Analysis Enhanced by Nullor Model and Modified Coates Flow Graph

, 2013
Irina Asenova, Franciszek Balik
openalex   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Acupuncture combined with antidepressants for mild-to-moderate depressive disorders: a systematic review with meta-analysis and trial sequential analysis. [PDF]

Front Neurol
Wang Y, Yu Y, Zhang S, Xi Q, Qiu J, Li X, Zhang M, Guo Y.   +7 more
europepmc   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Non-Parametric Goodness-of-Fit Tests Using Tsallis Entropy Measures. [PDF]

Entropy (Basel)
Cadirci MS.
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Mining exceptional social behavior on attributed interaction networks. [PDF]

Mach Learn
Atzmueller M, Centeio Jorge C, Rebelo de Sá C, Heravi BM, Gibson JL, Rossetti RJF.   +5 more
europepmc   +1 more source